127 citations · 208 across the 12 of their papers we have counts for
17 papers
Generalizing Bayesian Optimization with Decision-theoretic Entropies
Willie Neiswanger, Lantao Yu, Shengjia Zhao +2
Bayesian optimization (BO) is a popular method for efficiently inferring optima of an expensive black-box function via a sequence of queries. Existing information-theoretic BO proc…
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
Shengjia Zhao, Michael P. Kim, Roshni Sahoo +2
When facing uncertainty, decision-makers want predictions they can trust. A machine learning provider can convey confidence to decision-makers by guaranteeing their predictions are…
Improved Autoregressive Modeling with Distribution Smoothing
Chenlin Meng, Jiaming Song, Yang Song +2
While autoregressive models excel at image compression, their sample quality is often lacking. Although not realistic, generated images often have high likelihood according to the…
Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
Shengjia Zhao, Stefano Ermon
Decision makers often need to rely on imperfect probabilistic forecasts. While average performance metrics are typically available, it is difficult to assess the quality of individ…
Privacy Preserving Recalibration under Domain Shift
Rachel Luo, Shengjia Zhao, Jiaming Song +3
Classifiers deployed in high-stakes real-world applications must output calibrated confidence scores, i.e. their predicted probabilities should reflect empirical frequencies. Recal…
A Framework for Sample Efficient Interval Estimation with Control Variates
Shengjia Zhao, Christopher Yeh, Stefano Ermon
We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samp…